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6.
Results so far..
Patient characteristics
• Female gender is associated with higher HbA1c follow-up rates and a higher proportion of achieving the
recommended HbA1c level.
• The probability of HbA1c measurements increases with ageing. However, younger age increases the
probability of achieving the recommended HbA1c level.
• Distance is not a barrier to good control or to achieve treatment targets.
Socioeconomic variables
• The association of the area-level predictor of educational level and the
outcomes of care is closely comparable with the respective association of the
individual-level predictor of educational status.
7-class classification of urban and rural areas
• Best follow-up rates: peri-urban area, rural areas close to urban areas,
rural heartland areas.
• Worst follow-up rates: local centers in rural areas.
maija.toivakka@uef.fi 6
How to improve the situation and reduce costs?
STUDY 1

10.
teppo.repo@uef.fi
STUDY 3
Summary of study 3 and further research
Summary of study 3.
1. Eastern part of Finland continue to suffer a heavy CHD burden
2. Large disparities in CHD incidence rates between genders and SES groups (- > geography)
3. Spatial differences in the processes of care
4. Population dynamics / selective migration has increased spatial disparities in health
5. - > Rural-urban disparities may still be growing due to changing demography and SES.
Next steps:
1.What factors are causing the elevated clustering of CHD risk?
The spatial clustering of obesity: does the built environment matter? R. Huang et al. 2015
2. Is there improvement in the secondary prevention of CHD after 2014?
3. Is there a correlation between primary prevention of CHD (management of modifiable CHD risk
factors in the population) and CHD incidence rates?

11.
Comments from you? Next step: Does the greenness of the
living environment affect the care outcomes of T2DM?
• People who live in greener neighborhoods have smaller risk of getting type 2 diabetes
(Astell-Burt et al. 2014; Bodicoat et al. 2014; Müller et al. 2018).
- Does the neighborhood green space affect the care outcomes in T2DM patients?
• Data for green space
- Satellite image data (e.g. Normalized Difference Vegetation Index (NDVI))
- Land-use databases (e.g. calculating the percent of an area covered by parks/forests or
measuring the distance from a patient’s home to the nearest park)
- Comparison of indexes, Trabelsi 2018
• Is it possible to find an association in rather remote and green study region? (Forest
covers 89% of the land area in the study region.)
maija.toivakka@uef.fi 11
Astell-Burt et al. (2014). Is neighborhood green space associated with a lower risk of type 2 diabetes? Evidence from 267,072 Australians.
Diabetes Care 37(1):197-201. doi: 10.2337/dc13-1325.
Bodicoat et al. (2014). The association between neighbourhood greenspace and type 2 diabetes in a large cross-sectional study. BMJ Open
4(12):e006076. doi: 10.1136/bmjopen-2014-006076.
Müller et al. (2018). Inner-city green space and its association with body mass index and prevalent type 2 diabetes: a cross-sectional study in an
urban German city. BMJ Open 8(1):e019062. doi: 10.1136/bmjopen-2017-019
Trabelsi, Sonia (2018), On the measures of the Green, AAG 2018-04-10, Namur BE, U catolique de Louvain.
STUDY 1

12.
Comments from you to improve cost-efficency of care:
• The next stage in our research project:
markku.tykkylainen@uef.fi
Easy-to-use solutions in an interactive way, e.g. mobile-solutions to be tested in practise
T2DM, CHD, Artrial Fibrillation
Introducting cost-efficient solutions;
1) self-monitoring -> 2) telehealth (mobile), -> 3) e-feedback to patients
1. self-monitoring: good devices?, good practices?
2. telehealth (mobile): good apps?
3. e-feedback: what would be the best way to communicate? Virtual communities? Via
social media the patients used (tested in Uganda)?